Core Viewpoint - The debate surrounding AI agents is increasingly dominated by calls for comprehensive reform, including proposals to grant legal personhood to AI, as existing liability laws are seen as inadequate to address the challenges posed by AI technology [1] Group 1: Legal Framework and AI - The notion that existing liability systems are insufficient reflects a common pattern where new technologies prompt calls for specialized legal frameworks, often overlooking long-standing legal principles that have adapted to societal changes [1] - From a legal economics perspective, AI agents share significant normative similarities with traditional products, where developers are expected to prevent foreseeable harm, thus suggesting that existing negligence and product liability principles can be adjusted to address AI-related risks without complete overhaul [2][3] Group 2: Developer and User Responsibilities - Developers of AI systems are uniquely positioned to mitigate risks through their choices in training data, model design, and safety measures, aligning with the legal economic principle that developers are cost avoiders [3] - Users also bear responsibility; if they knowingly use AI agents with known error rates, it is inappropriate to solely hold developers accountable, indicating that liability rules should encourage responsible use of AI [4][5] Group 3: Challenges in Liability Assessment - The complexity and unpredictability of AI systems pose significant challenges for establishing causation and negligence, complicating the application of existing negligence laws [7][9] - Current liability frameworks, such as negligence and product liability, may not adequately address the unique characteristics of AI agents, necessitating careful consideration of how these laws apply to AI-related harm [10][12] Group 4: Reform Recommendations - A cautious approach is recommended, suggesting that existing regulatory mechanisms for AI agents should largely mirror those for traditional products, with adjustments to account for the complexities of AI systems [12][13] - The distinction between design and manufacturing defects may not apply to AI, as issues often arise from flawed training data, indicating a need for potential reforms in liability standards to ensure adequate incentives for safety [12][13]